--- /dev/null
+#include "models.h"
+
+void llama_model_nanbeige::load_arch_hparams(llama_model_loader & ml) {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ uint32_t n_loops_u = 1;
+ ml.get_key(LLM_KV_NUM_LOOPS, n_loops_u, false);
+ GGML_ASSERT(n_loops_u >= 1);
+
+ skip_loop_final_norm = false;
+ ml.get_key(LLM_KV_SKIP_LOOP_FINAL_NORM, skip_loop_final_norm, false);
+
+ n_layer_phys = (int) hparams.n_layer();
+
+ // Bound-check before casting: signed int mul can overflow and bypass the guard.
+ GGML_ASSERT((size_t) n_layer_phys * (size_t) n_loops_u <= (size_t) LLAMA_MAX_LAYERS);
+ n_loops = (int) n_loops_u;
+
+ // Expand logical layer count before load_tensors() allocates layers / KV.
+ if (n_loops > 1) {
+ for (int j = 1; j < n_loops; ++j) {
+ for (int i = 0; i < n_layer_phys; ++i) {
+ const int dst = i + j * n_layer_phys;
+ hparams.n_head_arr[dst] = hparams.n_head_arr[i];
+ hparams.n_head_kv_arr[dst] = hparams.n_head_kv_arr[i];
+ hparams.n_ff_arr[dst] = hparams.n_ff_arr[i];
+ hparams.is_swa_impl[dst] = hparams.is_swa_impl[i];
+ hparams.is_recr_impl[dst] = hparams.is_recr_impl[i];
+ }
+ }
+ hparams.n_layer_all = (uint32_t) ((size_t) n_layer_phys * (size_t) n_loops);
+ }
+
+ type = LLM_TYPE_UNKNOWN;
+}
+
+void llama_model_nanbeige::load_arch_tensors(llama_model_loader &) {
+ LLAMA_LOAD_LOCALS;
+
+ tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+ output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+ output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+ if (output == NULL) {
+ output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+ }
+
+ const int n_phys = n_layer_phys > 0 ? n_layer_phys : n_layer;
+ for (int i = 0; i < n_phys; ++i) {
+ auto & layer = layers[i];
+
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+ create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+ layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2},
+ TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
+
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
+ }
+
+ // Share physical weights across loops; each slot still has its own KV index.
+ if (n_loops > 1) {
+ for (int j = 1; j < n_loops; ++j) {
+ for (int i = 0; i < n_phys; ++i) {
+ layers[i + j * n_phys] = layers[i];
+ }
+ }
+ }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_nanbeige::build_arch_graph(const llm_graph_params & params) const {
+ return std::make_unique<graph>(*this, params);
+}
+
+llama_model_nanbeige::graph::graph(const llama_model & model, const llm_graph_params & params) :
+ llm_graph_context(params) {
+ const auto & nb = static_cast<const llama_model_nanbeige &>(model);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v();
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+
+ const int n_phys = nb.n_layer_phys > 0 ? nb.n_layer_phys : (int) n_layer;
+ const int n_loops = nb.n_loops > 0 ? nb.n_loops : 1;
+
+ ggml_tensor * cur;
+ ggml_tensor * inpL;
+
+ inpL = build_inp_embd(model.tok_embd);
+
+ ggml_tensor * inp_pos = build_inp_pos();
+
+ auto * inp_attn = build_attn_inp_kv();
+
+ const float kq_scale = hparams.f_attention_scale == 0.0f
+ ? 1.0f / sqrtf(float(n_embd_head))
+ : hparams.f_attention_scale;
+
+ ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+ for (int il = 0; il < n_layer; ++il) {
+ ggml_tensor * inpSA = inpL;
+
+ cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+ cb(cur, "attn_norm", il);
+
+ {
+ ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+ auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
+ n_embd_head, n_head, n_head_kv, il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, rope_factors,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, rope_factors,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ cur = build_attn(inp_attn,
+ model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
+ Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+ cb(cur, "attn_out", il);
+ }
+
+ if (il == n_layer - 1 && inp_out_ids) {
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = build_ffn(cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, model.layers[il].ffn_up_s,
+ model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, model.layers[il].ffn_gate_s,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_down_s,
+ NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+ cb(cur, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "ffn_out", il);
+
+ cur = build_cvec(cur, il);
+ cb(cur, "l_out", il);
+
+ inpL = cur;
+
+ if (n_loops > 1 &&
+ ((il + 1) % n_phys) == 0 &&
+ (il + 1) < n_layer &&
+ !nb.skip_loop_final_norm) {
+ cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, il);
+ cb(cur, "loop_norm", il);
+ inpL = cur;
+ }
+ }
+
+ cur = inpL;
+
+ cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+ cb(cur, "result_norm", -1);
+ res->t_embd = cur;
+
+ cur = build_lora_mm(model.output, cur, model.output_s);
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ ggml_build_forward_expand(gf, cur);
+}
overview / pkg / ggml / sources / llama.cpp / commitdiff